_base_ = [
    "../_base_/models/cascade_rcnn_r50_fpn.py",
    "../_base_/datasets/coco_detection.py",
    "../_base_/schedules/schedule_1x.py",
    "../_base_/default_runtime.py",
]
# model settings
model = dict(
    pretrained="torchvision://resnet101",
    backbone=dict(depth=101),
    roi_head=dict(
        bbox_head=[
            dict(
                type="SABLHead",
                num_classes=80,
                cls_in_channels=256,
                reg_in_channels=256,
                roi_feat_size=7,
                reg_feat_up_ratio=2,
                reg_pre_kernel=3,
                reg_post_kernel=3,
                reg_pre_num=2,
                reg_post_num=1,
                cls_out_channels=1024,
                reg_offset_out_channels=256,
                reg_cls_out_channels=256,
                num_cls_fcs=1,
                num_reg_fcs=0,
                reg_class_agnostic=True,
                norm_cfg=None,
                bbox_coder=dict(
                    type="BucketingBBoxCoder", num_buckets=14, scale_factor=1.7
                ),
                loss_cls=dict(
                    type="CrossEntropyLoss", use_sigmoid=False, loss_weight=1.0
                ),
                loss_bbox_cls=dict(
                    type="CrossEntropyLoss", use_sigmoid=True, loss_weight=1.0
                ),
                loss_bbox_reg=dict(type="SmoothL1Loss", beta=0.1, loss_weight=1.0),
            ),
            dict(
                type="SABLHead",
                num_classes=80,
                cls_in_channels=256,
                reg_in_channels=256,
                roi_feat_size=7,
                reg_feat_up_ratio=2,
                reg_pre_kernel=3,
                reg_post_kernel=3,
                reg_pre_num=2,
                reg_post_num=1,
                cls_out_channels=1024,
                reg_offset_out_channels=256,
                reg_cls_out_channels=256,
                num_cls_fcs=1,
                num_reg_fcs=0,
                reg_class_agnostic=True,
                norm_cfg=None,
                bbox_coder=dict(
                    type="BucketingBBoxCoder", num_buckets=14, scale_factor=1.5
                ),
                loss_cls=dict(
                    type="CrossEntropyLoss", use_sigmoid=False, loss_weight=1.0
                ),
                loss_bbox_cls=dict(
                    type="CrossEntropyLoss", use_sigmoid=True, loss_weight=1.0
                ),
                loss_bbox_reg=dict(type="SmoothL1Loss", beta=0.1, loss_weight=1.0),
            ),
            dict(
                type="SABLHead",
                num_classes=80,
                cls_in_channels=256,
                reg_in_channels=256,
                roi_feat_size=7,
                reg_feat_up_ratio=2,
                reg_pre_kernel=3,
                reg_post_kernel=3,
                reg_pre_num=2,
                reg_post_num=1,
                cls_out_channels=1024,
                reg_offset_out_channels=256,
                reg_cls_out_channels=256,
                num_cls_fcs=1,
                num_reg_fcs=0,
                reg_class_agnostic=True,
                norm_cfg=None,
                bbox_coder=dict(
                    type="BucketingBBoxCoder", num_buckets=14, scale_factor=1.3
                ),
                loss_cls=dict(
                    type="CrossEntropyLoss", use_sigmoid=False, loss_weight=1.0
                ),
                loss_bbox_cls=dict(
                    type="CrossEntropyLoss", use_sigmoid=True, loss_weight=1.0
                ),
                loss_bbox_reg=dict(type="SmoothL1Loss", beta=0.1, loss_weight=1.0),
            ),
        ]
    ),
)
